Next Steps

By completing this tutorial, you’ve learned how you can detect anomalous
behavior in a simple set of sample data. You created single and multi-metric
jobs in Kibana, which creates and opens jobs and creates and starts datafeeds for
you under the covers. You examined the results of the machine learning analysis in the
Single Metric Viewer and Anomaly Explorer in Kibana. You also
extrapolated the future behavior of a job by creating a forecast.

If you intend to use machine learning APIs in your applications, a good next step might be
to learn about the APIs by retrieving information about these sample jobs.
For example, the following APIs retrieve information about the jobs and datafeeds.

Ultimately, the next step is to start applying machine learning to your own data.
As mentioned in Identifying Data for Analysis, there are three things to consider when you’re
thinking about where machine learning will be most impactful:

It must be time series data.

It should be information that contains key performance indicators for the
health, security, or success of your business or system. The better you know the
data, the quicker you will be able to create jobs that generate useful
insights.

Ideally, the data is located in Elasticsearch and you can therefore create a datafeed
that retrieves data in real time. If your data is outside of Elasticsearch, you
cannot use Kibana to create your jobs and you cannot use datafeeds. Machine
learning analysis is still possible, however, by using APIs to create and manage
jobs and to post data to them.

Once you have decided which data to analyze, you can start considering which
analysis functions you want to use. For more information, see Function Reference.

In general, it is a good idea to start with single metric jobs for your
key performance indicators. After you examine these simple analysis results,
you will have a better idea of what the influencers might be. You can create
multi-metric jobs and split the data or create more complex analysis functions
as necessary. For examples of more complicated configuration options, see
Configuring Machine Learning.